Devices and systems for voice over internet protocol for identifying network traffic

ABSTRACT

Devices and systems for voice over Internet protocol (VoIP) for identifying network traffic are described herein. One or more embodiments include a VoIP device for identifying network traffic comprising a signal monitor to identify a signaling protocol from the network traffic and an artificial intelligence engine configured to receive signaling protocol sample data to train a signal artificial intelligence (AI) model and process the signaling protocol identified by the signal monitor in the signal AI model to identify the network traffic.

TECHNICAL FIELD

The present disclosure relates to devices and systems for voice overInternet protocol for identifying network traffic.

BACKGROUND

Many organizations use a voice over Internet protocol (VoIP)communication system to communicate rather than a traditional telephonecommunication system. As with all communication systems, the ability toconnect the parties wanting to speak to each other over a VoIP systemand to maintain that connection, during their intended communicationperiod, is important. One key to connecting and maintaining thatconnection are identifying network traffic to provide a better qualityof service (QoS).

Some systems provide varying QoS, for example, based on the type ofnetwork traffic being communicated on the VoIP system. In some examples,better QoS can be provided to media traffic over non-media traffic.Media traffic can include transmitting audio and video data. In somesuch systems, the QoS determination can be used to control factors, suchas bandwidth, transmission delays, jitter, and/or packet loss, that canimpact performance of the network traffic.

VoIP communication resources can be underutilized when a QoS is randomlyprovided to network. For example, non-media network traffic is not assensitive as media network traffic to transmission delays, jitters, andpacket loss. As described above, transmission delays, jitters, andpacket loss can be minimized by providing the network traffic with anincreased QoS. In some examples, non-media network traffic could beprovided a higher QoS than media network traffic, wasting VoIPcommunication resources that could be used to minimize transmissiondelays, jitters, and packet loss for media network traffic.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example of a system including voice over Internetprotocol (VoIP) devices according to one or more embodiments of thepresent disclosure.

FIG. 2 illustrates an example of a device for identifying networktraffic according to one or more embodiments of the present disclosure.

FIG. 3 illustrates an example of a system for identifying networktraffic according to one or more embodiments of the present disclosure.

DETAILED DESCRIPTION

In the embodiments of the present disclosure, an edge device and/orother voice over internet protocol (VoIP) device can be included in acommunication system. The communication system can also include a numberof networks that can be connected to the VoIP device. The number ofnetworks can be used by a party using an end device to communicate withanother party, via network traffic, through an intermediary device.

The VoIP device can be used for identifying network traffic. The devicecan include a signal monitor to identify a signaling protocol from thenetwork traffic.

In some examples, the device can also include an artificial intelligenceengine. The artificial intelligence engine can be configured to receivesignal protocol sample data to train a signal artificial intelligence(AI) model and process the signaling protocol identified by the signalmonitor in the signal AI model to identify the types of informationbeing passed in packets within the network traffic.

The signal protocol sample data can include a plurality of randomlyselected signal protocol bit patterns, for example. Training of thesignal AI model is complete in response to the signal AI model receivinga lowest error and highest probability in identifying the plurality ofrandomly selected signal protocol bit patterns.

In some embodiments, the media AI model may be used when the improvementmade during training improves less than a minimum threshold value ineither the error rate or the probability. In some examples, the signalAI model can be retrained in response to receiving new signal protocoldata.

In some embodiments, the signal AI model can include using a logisticregression machine. This logistic regression machine can be numericalcomputation logic running on hardware that supports mathematicalfunctions, for example. The types of packets in the network traffic canbe identified based on a bit pattern of the signal protocol. Forexample, the bit pattern can include a payload. The payload can be asequence of bytes that define a bit pattern. In some examples, thepayload includes packet data except network transport headers andmeta-data.

The signal AI model can, for example, identify the type of packets inthe network traffic as media or non-media. Network traffic that is mediacan, for example, include audio and/or video data. In response to thesignal AI identifying the network traffic as media, increased quality ofservice (QoS) can be provided.

The device can also include a media monitor. The media monitor canidentify a media protocol which can be a bit pattern, for example.

As discussed above, in some embodiments, the device can include anartificial intelligence engine. The artificial intelligence engine canbe configured to receive media protocol sample data to train a mediaartificial intelligence (AI) model. For purposes of training the engine,the media protocol sample data can include a plurality of randomlyselected media protocol bit patterns, for example.

Training of the media AI model is complete in response to the media AImodel receiving a lowest error and highest probability in identifyingthe plurality of randomly selected media protocol bit patterns as media.In some examples, the media AI model can be retrained in response toreceiving new media protocol data.

The media AI model can include using a logistic regression machine. Themedia AI model can process the media protocol identified by the mediamonitor to identify network traffic. Network traffic can be identifiedas media or non-media, for example.

The media AI model can identify the network traffic based on bit patternof the media protocol. For example, the bit pattern can include apayload. In some examples, the media protocol can be processed by themedia AI model in response to the signaling protocol being unavailable.For example, if the signaling protocol is present, the signalingprotocol can convey the media network traffic information used toidentify the network traffic as media and as a result there is no needto run the media AI model.

In some embodiments, increased quality of service (QoS) can be providedin response to the media AI model identifying the network traffic asmedia. Voice and video data can be included as media and can besensitive to delays, jitters, and packet loss, for example. Providingbetter QoS can minimize these defects for a better user experience.

In some embodiments, a system for identifying network traffic caninclude a network interface. The network interface can be configured tocapture the network traffic and send a payload from the network trafficto a signal AI engine within the system to determine what level of QoSthe network traffic should receive. The signal AI engine can beconfigured to receive the payload and identify the network traffic basedon learning the signal AI model from known media network trafficpatterns.

The system can also include a media AI engine. The media AI engine canbe configured to receive the payload and identify the type of networktraffic in response to the signal AI engine failing. As described above,the media AI engine can be used as a backup for the signal AI engine.

In some embodiments, the media AI engine can be further configured toextract Internet protocol (IP) and/or port information. The IP and/orport information can be sent to a QoS enforcer. A QoS enforcer can be asystem of queues associated with an interface connected to a link, whereeach queue can have priority attached. A high priority queue can bechosen first for sending a packet to a link and a low priority queue canbe chosen last for sending a packet to a link, for example.

The signal AI engine and/or the media AI engine can calculate aprobability that the type of network traffic was identified correctly.The network traffic packet type can be identified as media in responseto the probability value exceeding a threshold. The network trafficpacket type can be identified as non-media in response to theprobability failing to exceed a threshold. In response to identifyingthe network traffic packet type, the bandwidth provided can be increasedor decreased in an attempt to better accommodate the media type.

In this manner, the QoS of the system can be adjusted more precisely,based on the information obtained about the types of packets that arebeing moved in the network traffic. This can make the networkcommunications of the system more efficient, which can reduce monetaryand/or equipment usage costs, among other benefits.

In the following portion of the detailed description, reference is madeto the accompanying figures that form a part hereof. The figures show byway of illustration how one or more embodiments of the disclosure may bepracticed.

These embodiments are described in sufficient detail to enable those ofordinary skill in the art to practice one or more embodiments of thisdisclosure. It is to be understood that other embodiments may beutilized and that process changes may be made without departing from thescope of the present disclosure.

As will be appreciated, elements shown in the various embodiments hereincan be added, exchanged, combined, and/or eliminated so as to provide anumber of additional embodiments of the present disclosure. Theproportion and the relative scale of the elements provided in thefigures are intended to illustrate the embodiments of the presentdisclosure and should not be taken in a limiting sense.

Also, as used herein, “a” or “a number of” something can refer to one ormore such things. For example, “a number of operations” can refer to oneor more operations.

FIG. 1 illustrates an example of a system including voice over Internetprotocol (VoIP) devices according to one or more embodiments of thepresent disclosure. In the embodiment illustrated in FIG. 1, the system100 includes a number of VoIP devices 101, a number of networks 102-1,102-N (e.g., WAN networks), a number of network links 104-1, 104-P(wired or wireless), a number of local networks 106, a number of endcommunication devices that a party may use to communicate with anotherparty over a connection through one of the network links 104-1, 104-P,and a number of intermediary devices 110 that provide a pathway to allowthe passing of packets between networks 102-1, 102-N.

The system 100, shown in FIG. 1, includes two network connections (vianetworks 102-1 and 102-N) that can be used by a party using the enddevice 108 to communicate with another party on a network that iscommunicating through intermediary device 110. In this manner, acommunication session can be created between two parties, allowing themto communicate with each other.

As can be seen from the illustration in FIG. 1, a VoIP device 101 isconnected to links to several networks. For instance, link 104-1 allowsconnection to network 102-1 and a unique IP address is provided toidentify the device 101 with respect to that network. Link 104-P allowsconnection to network 102-N and a unique IP address (different from thatused for network 102-1) is provided to identify the VoIP device 101 withrespect to that network. Additionally, VoIP device 101 is also connectedto a local network (LAN) through link 106 and a unique IP address(different from those used for networks 102-1, 102-N) is provided toidentify the device 101 with respect to that network.

In some examples, the VoIP device 101 can include a processor 103 and amemory 105. The memory 105 can have various types of informationincluding data and executable instructions.

The processor 103 can execute instructions that are stored on aninternal or external non-transitory computer readable medium (CRM). Anon-transitory CRM can include volatile and/or non-volatile memory.

Volatile memory can include memory that depends upon power to storeinformation, such as various types of dynamic random access memory(DRAM), among others. Non-volatile memory can include memory that doesnot depend upon power to store information.

The memory 105 and/or the processor 103 may be located on the VoIPdevice 101 or off of the VoIP device 101, in some embodiments. Theprocessor 103 can execute instructions stored on the memory 105 toidentify a signaling protocol from the network traffic, receivesignaling protocol sample data to train a signal AI model, and processthe signaling protocol identified by the signal monitor in the signalartificial AI model to identify the network traffic, for example.

FIG. 2 illustrates an example of a device for identifying networktraffic according to one or more embodiments of the present disclosure.The device 201 can be, for example, an edge device (sitting at the edgeof a local area network and a wide area network) and/or another type ofVoIP device. The device 201 can be a router, for example. The device 201can be in the path of the network traffic or can receive a sample (apacket or portion thereof) of the network traffic from a differentdevice in the path of the network traffic.

In some examples, the device 201 can include a signal monitor 212, amedia monitor 214, and an artificial intelligence engine 216. The signalmonitor 212 of the device 201 can capture network packets and can beutilized to identify a signaling protocol from the network traffic. Thesignaling protocol can be a protocol that defines standards forexchanging communication parameters and media capabilities between twoparties, for example.

The artificial intelligence engine 216 can include a signal AI model218, a media AI model 220, a logistic regression machine 222, and aprobability calculator 224. The artificial intelligence engine 216 canbe configured to receive signal protocol sample data. The signalprotocol sample data can be used to train the signal AI model 218.

The artificial intelligence engine 216 can also process the signalingprotocol identified by the signal monitor 212 in the signal AI model 218to identify the network traffic.

The signal protocol sample data can include a plurality of randomlyselected signal protocol bit patterns, for example. Training of thesignal AI model 218 is complete in response to the signal AI model 218receiving a lowest error and highest probability in identifying theplurality of randomly selected signal protocol bit patterns as media. Insome examples, the signal AI model 218 can be retrained in response toreceiving new signal protocol data.

In some embodiments, the artificial intelligence engine 216 can includea logistic regression machine 222. The signal AI model 218 can use thelogistic regression machine 222. The logistic regression machine 222 canestimate the parameters of a logistic model.

For example, the logistic model can estimate the probability of a binaryresponse based on one or more predictor features. A training set is usedto create an optimal set of parameters that minimizes error andincreases the probability of a particular outcome. For a given set oftest input features, if the computed probability is above a certainthreshold, the input can be classified as a media type.

Network traffic can be identified based on bit pattern of the signalprotocol. For example, the bit pattern can include a sequence of bytesin a payload. The signal AI model 218 can identify the network trafficas media or non-media. Network traffic as media can include audio and/orvideo data. In response to the signal AI model 218 identifying thenetwork traffic as media, increased quality of service (QoS) can beprovided.

The device 201 can include a media monitor 214. The media monitor 214can identify a media protocol, which can be a bit pattern, for example.

The artificial intelligence engine 216 can include a media artificialintelligence (AI) model 220. The artificial intelligence engine 216 canbe configured to receive media protocol sample data to train the mediaAI model 220. The media protocol sample data can include a plurality ofrandomly selected media protocol bit patterns, for example.

As discussed above, training of the media AI model 220 is complete inresponse to the media AI model 220 receiving a lowest error and highestprobability in identifying the plurality of randomly selected mediaprotocol bit patterns as media. In some examples, the media AI model 220can be retrained in response to receiving new media protocol data.

The media AI model 220 can include using the logistic regression machine222. The logistic regression machine analyzes the one or more randomlyselected media protocol bit patterns and identifies them as media ornon-media. The results can be reviewed periodically by a human user or acomputing device to help precision the accuracy of the determinationbetween media and non-media. As discussed above, if the probability ofdetermining the type of data or just to update the model with new datatypes, new training data can be introduced to the media AI model 220. Inthis manner, the media AI model 220 can process the media protocolidentified by the media monitor 214 to identify network traffic. Networktraffic can be identified as media or non-media, for example.

The media AI model 220 can identify the network traffic based on bitpattern of the media protocol. For example, the bit pattern can includea sequence of bytes in a payload. In some examples, the media protocolcan be processed by the media AI model 220 in response to the signalingprotocol being unavailable.

In some embodiments, increased quality of service (QoS) can be providedin response to the media AI model 220 identifying the network traffic asmedia.

FIG. 3 illustrates an example of a system for identifying networktraffic according to one or more embodiments of the present disclosure.The system 330 can include a device 301, signal protocol sample data338, media protocol sample data 340, and network traffic 332.

The network traffic 332 can include a signal protocol 334 and a mediaprotocol 336. The signal protocol 334 can include a bit pattern 342-1and the bit pattern 342-1 can include, for example, a payload 344-1. Themedia protocol 336 can include a bit pattern 342-2 and the bit pattern342-2 can include, for example, a payload 344-2.

The device 301 can include a network interface 346. The networkinterface 346 can be configured to capture the network traffic 332 andsend the bit pattern 342-1 and/or the payload 344-1 from the networktraffic 332 to a signal AI model 316-1.

The device 301 can include a signal AI engine 316-1 configured toreceive the bit pattern 342-1 and/or the payload 344-1 and identify thenetwork traffic 332. The device can also include a media AI engine316-2. The media AI engine 316-2 can be configured to receive the bitpattern 342-2 and/or the payload 344-2 and identify the network traffic332 in response to the signal AI engine 316-1 failing.

In some embodiments, the media AI engine 316-2 can be further configuredto extract IP and/or port information from the network traffic 332. TheIP and/or port information can be included in the network traffic 332.The IP and/or port data can be sent to a QoS enforcer, for example. TheQoS enforcer can be a system of queues associated with an interfaceconnected to a link, where each queue can have priority attached. A highpriority queue can be chosen first for sending a packet to a link and alow priority queue can be chosen last for sending a packet to a link,for example.

The signal AI engine 316-1 and/or the media AI engine 316-2 cancalculate a probability that the network traffic 332 was identifiedcorrectly. The network traffic 332 can be identified as media inresponse to the probability exceeding a threshold. The network traffic332 can be identified as non-media in response to the probabilityfailing to exceed a threshold. In response to identifying the networktraffic 332, bandwidth provided is increased or decreased.

Although specific embodiments have been illustrated and describedherein, those of ordinary skill in the art will appreciate that anyarrangement calculated to achieve the same techniques can be substitutedfor the specific embodiments shown. This disclosure is intended to coverany and all adaptations or variations of various embodiments of thedisclosure.

It is to be understood that the above description has been made in anillustrative fashion, and not a restrictive one. Combination of theabove embodiments, and other embodiments not specifically describedherein will be apparent to those of skill in the art upon reviewing theabove description.

The scope of the various embodiments of the disclosure includes anyother applications in which the above structures and methods are used.Therefore, the scope of various embodiments of the disclosure should bedetermined with reference to the appended claims, along with the fullrange of equivalents to which such claims are entitled.

In the foregoing Detailed Description, various features are groupedtogether in example embodiments illustrated in the figures for thepurpose of streamlining the disclosure. This method of disclosure is notto be interpreted as reflecting an intention that the embodiments of thedisclosure require more features than are expressly recited in eachclaim.

Rather, as the following claims reflect, inventive subject matter liesin less than all features of a single disclosed embodiment. Thus, thefollowing claims are hereby incorporated into the Detailed Description,with each claim standing on its own as a separate embodiment.

What is claimed:
 1. A voice over Internet protocol (VoIP) device foridentifying network traffic, comprising: a media monitor to identify amedia protocol from the network traffic; a signal monitor to identify asignaling protocol from the network traffic; and an artificialintelligence (AI) engine configured to: receive signaling protocolsample data to train a signal artificial intelligence (AI) model;process the signaling protocol identified by the signal monitor in thesignal artificial intelligence (AI) model to identify the networktraffic in response to the training of the signal artificialintelligence (AI) model improving at least a minimum threshold value inan error rate or probability in identifying a plurality of randomlyselected signal protocol bit patterns; receive media protocol sampledata to train a media artificial intelligence (AI) model; and processthe media protocol identified by the media monitor in the mediaartificial intelligence (AI) model to identify the network traffic inresponse to the training of the signal artificial intelligence (AI)model improving less than the minimum threshold value in the error rateand probability in identifying the plurality of randomly selected signalprotocol bit patterns.
 2. The device of claim 1, wherein the signal AImodel identifies the network traffic as media or non-media.
 3. Thedevice of claim 2, wherein increased quality of service (QoS) isprovided in response to the signal AI model identifying the networktraffic as media.
 4. The device of claim 2, wherein media includes audioor video data.
 5. The device of claim 1, wherein the AI engine isconfigured to process the media protocol identified by the media monitorin response to the signaling protocol being unavailable.
 6. An edgedevice for identifying network traffic, comprising: a media monitor toidentify a media protocol from the network traffic; and an artificialintelligence engine configured to: receive media protocol sample data totrain a media artificial intelligence (AI) model; and process the mediaprotocol identified by the media monitor in the media artificialintelligence (AI) model to identify the network traffic in response totraining of a signal artificial intelligence (AI) model improving lessthan a minimum threshold value in an error rate or probability inidentifying a plurality of randomly selected signal protocol bitpatterns.
 7. The device of claim 6, wherein training the media AI modelincludes using a logistic regression machine.
 8. The device of claim 6,wherein the media AI model identifies the network traffic based on bitpattern of the media protocol.
 9. The device of claim 8, wherein the bitpattern can include a payload.
 10. The device of claim 6, wherein themedia protocol sample data includes a plurality of randomly selectedmedia protocol bit patterns.
 11. The device of claim 10, whereintraining of the media AI model is complete in response to the media AImodel receiving a lowest error and highest probability in identifyingthe plurality of randomly selected media protocol bit patterns as media.12. The device of claim 6, wherein the media AI model is retrained inresponse to receiving new media protocol data.
 13. The device of claim6, wherein the media AI model identifies the network traffic as media ornon-media.
 14. The device of claim 6, wherein increased quality ofservice (QoS) is provided in response to the media AI model identifyingthe network traffic as media.
 15. A system for identifying networktraffic, comprising: a network interface configured to capture thenetwork traffic and send a payload from the network traffic; a signalartificial intelligence (AI) engine configured to receive signalingprotocol sample data to train a signal artificial intelligence (AI)model, receive the payload, and identify the network traffic; and amedia artificial intelligence (AI) engine configured to receive thepayload and identify the network traffic in response to training of thesignal AI model improving less than a minimum threshold value in anerror rate or probability in identifying a plurality of randomlyselected signal protocol bit patterns.
 16. The system of claim 15,wherein the media AI engine is further configured to extract Internetprotocol (IP) or port information.
 17. The system of claim 16, whereinthe network interface is further configured to send the IP or portinformation to a Quality of Service (QoS) enforcer.
 18. The system ofclaim 15, wherein the signal AI engine calculates a probability that thesignal AI engine correctly identified the network traffic.
 19. Thesystem of claim 18, wherein the network traffic is identified as mediain response to the probability exceeding a threshold.
 20. The system ofclaim 18, wherein the network traffic is identified as non-media inresponse to the probability failing to exceed a threshold.